code: def testSVD_PreservesPretrainedWeights(self):
      i = tf.keras.layers.Input(shape=(2), name="input")
      output = tf.keras.layers.Dense(3, name="fc1")(i)
      model = tf.keras.Model(inputs=[i], outputs=[output])
  
      dense_layer_weights = model.layers[1].get_weights()
  
      algorithm = svd.SVD(rank=1, update_freq=1, warmup_step=10)
      training_model = algorithm.optimize_model(model)
  
      dense_layer_training_weights = training_model.layers[
          1
      ].get_weights()
  
      # kernel
      algorithm.weight_reprs = []
      algorithm.init_training_weights(dense_layer_weights[0])
      w1_repr, w2_repr = algorithm.weight_reprs
      assert (
          (
              w1_repr.kwargs["initializer"](None)
              == dense_layer_training_weights[0]
          )
          .numpy()
          .all()
      )
      assert (
          (
              w2_repr.kwargs["initializer"](None)
              == dense_layer_training_weights[1]
          )
          .numpy()
          .all()
      )
  
      # bias
      assert (
          dense_layer_weights[1] == dense_layer_training_weights[2]
      ).all()
  
file_path: model-optimization/tensorflow_model_optimization/python/core/common/keras/compression/algorithms/periodical_update_and_scheduling_test.py
imports: import os
  import tempfile
  import tensorflow
  from tensorflow_model_optimization.python.core.common.keras.compression.algorithms import periodical_update_and_scheduling
  from tensorflow_model_optimization.python.core.keras.testing import test_utils_mnist
label_desc: Use image node_id 6 for calling the FunctionalTest obj's underlying member method code with example usage: obj.testSVD_PreservesPretrainedWeights() without return types